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The Normal Approximation to the Binomial Distribution 39.2    The Normal Approximation to the Binomial Distribution 39.2 Introduction We have already seen that the Poisson distribution can be used to approximate the binomial distri- bution for large values of n and small values of p provided that the correct conditions exist. The approximation is only of practical use if just a few terms of the Poisson distribution need be calcu- lated. In cases where many - sometimes several hundred - terms need to be calculated the arithmetic involved becomes very tedious indeed and we turn to the normal distribution for help. It is possible, of course, to use high-speed computers to do the arithmetic but the normal approximation to the binomial distribution negates the necessity of this in a fairly elegant way. In the problem situations which follow this introduction the normal distribution is used to avoid very tedious arithmetic while at the same time giving a very good approximate solution. # • be familiar with the normal distribution and Prerequisites the standard normal distribution Before starting this Section you should ... • be able to calculate probabilities using the standard normal distribution '" • recognise when it is appropriate to use the $! normal approximation to the binomial distribution Learning Outcomes • solve problems using the normal approximation to the binomial distribution. On completion you should be able to ... • interpret the answer obtained using the normal approximation in terms of the original problem & % 26 HELM (2008): Workbook 39: The Normal Distribution ® 1. The normal approximation to the binomial distribution A typical problem An engineering professional body estimates that 75% of the students taking undergraduate engineer- ing courses are in favour of studying of statistics as part of their studies. If this estimate is correct, what is the probability that more than 780 undergraduate engineers out of a random sample of 1000 will be in favour of studying statistics? Discussion The problem involves a binomial distribution with a large value of n and so very tedious arithmetic may be expected. This can be avoided by using the normal distribution to approximate the binomial distribution underpinning the problem. If X represents the number of engineering students in favour of studying statistics, then X ∼ B(1000, 0.75) Essentially we are asked to find the probability that X is greater than 780, that is P(X > 780). The calculation is represented by the following statement P(X > 780) = P(X = 781) + P(X = 782) + P(X = 783) + ··· + P(X = 1000) In order to complete this calculation we have to find all 220 terms on the right-hand side of the expression. To get some idea of just how big a task this is when the binomial distribution is used, imagine applying the formula n(n − 1)(n − 2) ... (n − r + 1)pr(1 − p)n−r P(X = r) = r(r − 1)(r − 2) ... 3.2.1 220 times! You would have to take n = 1000, p = 0.75 and vary r from 781 to 1000. Clearly, the task is enormous. Fortunately, we can approximate the answer very closely by using the normal distribution with the same mean and standard deviation as X ∼ B(1000, 0.75). Applying the usual formulae for µ and σ we obtain the values µ = 750 and σ = 13.7 from the binomial distribution. We now have two distributions, X ∼ B(1000, 0.75) and (say) Y ∼ N(750, 13.72). Remember that the second parameter represents the variance. By doing the appropriate calculations, (this is extremely tedious even for one term!) it can be shown that P(X = 781) ≈ P(780.5 ≤ Y ≤ 781.5) This statement means that the probability that X = 781 calculated from the binomial distribution X ∼ B(1000, 0.75) can be very closely approximated by the area under the normal curve Y ∼ N(750, 13.72) between 780.5 and 781.5. This relationship is then applied to all 220 terms involved in the calculation. HELM (2008): 27 Section 39.2: The Normal Approximation to the Binomial Distribution The result is summarised below: P(X = 781) ≈ P(780.5 ≤ Y ≤ 781.5) P(X = 782) ≈ P(781.5 ≤ Y ≤ 782.5) . P(X = 999) ≈ P(998.5 ≤ Y ≤ 999.5) P(X = 1000) ≈ P(999.5 ≤ Y ≤ 1000.5) By adding these probabilities together we get P(X > 780) = P(X = 781) + P(X = 782) + ··· + P(X = 1000) ≈ P(780.5 ≤ Y ≤ 1000.5) To complete the calculation we need only to find the area under the curve Y ∼ N(750, 13.72) between the values 780.5 and 1000.5. This is far easier than completing the 220 calculations suggested by the use of the binomial distribution. Finding the area under the curve Y ∼ N(750, 13.72) between the values 780.5 and 1000.5 is easily done by following the procedure used previously. The calculation, using the tables on page 15 and working to three decimal places, is 780.5 − 750 1000.5 − 750 P(X > 780) ≈ P( ≤ Z ≤ ) 13.7 13.7 = P(2.23 ≤ Z ≤ 18.28) = P(Z ≥ 2.23) = 0.013 Notes: 1. Since values as high as 18.28 effectively tell us to find the area to the right of 2.33 (the area to the right of 18.28 is so close to zero as to make no difference) we have P(Z ≥ 2.23) = 0.0129 ≈ 0.013 2. The solution given assumes that the original binomial distribution can be approximated by a normal distribution. This is not always the case and you must always check that the following conditions are satisfied before you apply a normal approximation. The conditions are: • np > 5 • n(1 − p) > 5 You can see that these conditions are satisfied here. 28 HELM (2008): Workbook 39: The Normal Distribution ® Task A particular production process used to manufacture ferrite magnets used to op- erate reed switches in electronic meters is known to give 10% defective magnets on average. If 200 magnets are randomly selected, what is the probability that the number of defective magnets is between 24 and 30? Your solution Answer If X is the number of defective magnets then X ∼ B(200, 0.1) and we require P(24 < X < 30) = P(25 ≤ X ≤ 29) Now, √ µ = np = 200 × 0.1 = 20 and σ = pnp(1 − p) = 200 × 0.1 × 0.9 = 4.24 Note that np > 5 and n(1 − p) > 5 so that approximating X ∼ B(200, 0.1) by Y ∼ N(20, 4.242) is acceptable. We can approximate X ∼ B(200, 0.1) by the normal distribution Y ∼ N(20, 4.242) and use the transformation Y − 20 Z = ∼ N(0, 1) 4.24 so that P(25 ≤ X ≤ 29) ≈ P(24.5 ≤ Y ≤ 29.5) 24.5 − 20 29.5 − 20 = P( ≤ Z ≤ ) 4.24 4.24 = P(1.06 ≤ Z ≤ 2.24) = 0.4875 − 0.3554 = 0.1321 HELM (2008): 29 Section 39.2: The Normal Approximation to the Binomial Distribution Example 15 Overbooking of passengers on intercontinental flights is a common practice among airlines. Aircraft which are capable of carrying 300 passengers are booked to carry 320 passengers. If on average 10% of passengers who have a booking fail to turn up for their flights, what is the probability that at least one passenger who has a booking will end up without a seat on a particular flight? Solution Let p = P(a passenger with a booking, fails to turn up) = 0.10. Then: q = P(a passenger with a booking, turns up) = 1 − p = 1 − 0.10 = 0.9 Let X = number of passengers with a booking who turn up. As there are 320 bookings, we are dealing with the terms of the binomial expansion of 320 × 319 (q + p)320 = q320 + 320q319p + q318p2 + ··· + p320 2! Using this approach is too long to calculate by finding the values term by term. It is easier to switch to the corresponding normal distribution, i.e. that which has the same mean and variance as the binomial distribution above. Mean = µ = 320 × 0.9 = 288 √ Variance = 320 × 0.9 × 0.1 = 28.8 so σ = 28.8 = 5.37 Hence, the corresponding normal distribution is given by Y ∼ N(288, 28.8) 300.5 − 288 So that, P(X > 300) ≈ P(Y ≥ 300.5) = P(Z ≥ ) = P(Z ≥ 2.33) 5.37 From Z-tables P(Z ≥ 2.33) = 0.0099. NB. Continuity correction is needed when changing from the binomial, a discrete distribution, to the normal, a continuous distribution. 30 HELM (2008): Workbook 39: The Normal Distribution ® Exercises 1. The diameter of an electric cable is normally distributed with mean 0.8 cm and variance 0.0004 cm2. (a) What is the probability that the diameter will exceed 0.81 cm? (b) The cable is considered defective if the diameter differs from the mean by more than 0.025 cm. What is the probability of obtaining a defective cable? 2. A machine packs sugar in what are nominally 2 cm kg bags. However there is a variation in the actual weight which is described by the normal distribution. (a) Previous records indicate that the standard deviation of the distribution is 0.02 cm kg and the probability that the bag is underweight is 0.01. Find the mean value of the distribution. (b) It is hoped that an improvement to the machine will reduce the standard deviation while allowing it to operate with the same mean value. What value standard deviation is needed to ensure that the probability that a bag is underweight is 0.001? 3. Rods are made to a nominal length of 4 cm but in fact the length is a normally distributed random variable with mean 4.01 cm and standard deviation 0.03.
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